Ranking Adverse Drug Reactions With Crowdsourcing

Handle URI:
http://hdl.handle.net/10754/550418
Title:
Ranking Adverse Drug Reactions With Crowdsourcing
Authors:
Gottlieb, Assaf ( 0000-0003-4904-631X ) ; Hoehndorf, Robert ( 0000-0001-8149-5890 ) ; Dumontier, Michel ( 0000-0003-4727-9435 ) ; Altman, Russ B ( 0000-0003-3859-2905 )
Abstract:
Background: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Ranking Adverse Drug Reactions With Crowdsourcing 2015, 17 (3):e80 Journal of Medical Internet Research
Publisher:
JMIR Publications Inc.
Journal:
Journal of Medical Internet Research
Issue Date:
23-Mar-2015
DOI:
10.2196/jmir.3962
PubMed ID:
25800813
PubMed Central ID:
PMC4387295
Type:
Article
ISSN:
1438-8871
Additional Links:
http://www.jmir.org/2015/3/e80/
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorGottlieb, Assafen
dc.contributor.authorHoehndorf, Roberten
dc.contributor.authorDumontier, Michelen
dc.contributor.authorAltman, Russ Ben
dc.date.accessioned2015-04-21T14:02:14Zen
dc.date.available2015-04-21T14:02:14Zen
dc.date.issued2015-03-23en
dc.identifier.citationRanking Adverse Drug Reactions With Crowdsourcing 2015, 17 (3):e80 Journal of Medical Internet Researchen
dc.identifier.issn1438-8871en
dc.identifier.pmid25800813en
dc.identifier.doi10.2196/jmir.3962en
dc.identifier.urihttp://hdl.handle.net/10754/550418en
dc.description.abstractBackground: There is no publicly available resource that provides the relative severity of adverse drug reactions (ADRs). Such a resource would be useful for several applications, including assessment of the risks and benefits of drugs and improvement of patient-centered care. It could also be used to triage predictions of drug adverse events. Objective: The intent of the study was to rank ADRs according to severity. Methods: We used Internet-based crowdsourcing to rank ADRs according to severity. We assigned 126,512 pairwise comparisons of ADRs to 2589 Amazon Mechanical Turk workers and used these comparisons to rank order 2929 ADRs. Results: There is good correlation (rho=.53) between the mortality rates associated with ADRs and their rank. Our ranking highlights severe drug-ADR predictions, such as cardiovascular ADRs for raloxifene and celecoxib. It also triages genes associated with severe ADRs such as epidermal growth-factor receptor (EGFR), associated with glioblastoma multiforme, and SCN1A, associated with epilepsy. Conclusions: ADR ranking lays a first stepping stone in personalized drug risk assessment. Ranking of ADRs using crowdsourcing may have useful clinical and financial implications, and should be further investigated in the context of health care decision making.en
dc.publisherJMIR Publications Inc.en
dc.relation.urlhttp://www.jmir.org/2015/3/e80/en
dc.rightsThis is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.en
dc.subjectdrug side effectsen
dc.subjectalert fatigueen
dc.subjectpharmacovigilanceen
dc.subjectadverse drug reactionsen
dc.subjectpatient-centered careen
dc.subjectcrowdsourcingen
dc.titleRanking Adverse Drug Reactions With Crowdsourcingen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Medical Internet Researchen
dc.identifier.pmcidPMC4387295en
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Genetics, Stanford University, Stanford, CA, United Statesen
dc.contributor.institutionStanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, United Statesen
dc.contributor.institutionDepartments of Genetics and Bioengineering, Stanford University, Stanford, CA, United Statesen
kaust.authorHoehndorf, Roberten

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